Importance of accounting for loss to follow-up when comparing mortality between immigrants and long-term residents: a population-based retrospective cohort
Bibliographic record
Abstract
OBJECTIVES: To evaluate the association between immigration status and all-cause mortality in different disease cohorts, and the impact of loss to follow-up on the observed associations. DESIGN: Population-based retrospective cohort study using linked administrative health data in Ontario, Canada. SETTING: We followed adults with a first-ever diagnosis of ischaemic stroke, cancer or schizophrenia between 2002 and 2013 from index event to death, loss to follow-up, or end of follow-up in 2018. PRIMARY AND SECONDARY OUTCOME MEASURES: Our outcomes of interest were all-cause mortality and loss to follow-up. For each disease cohort, we calculated adjusted HRs of death in immigrants compared with long-term residents, adjusting for demographic characteristics and comorbidities, with and without censoring for those who were lost to follow-up. We calculated the ratio of two the HRs and the respective CL using bootstrapping methods. RESULTS: Immigrants were more likely to be lost to follow-up than long-term residents in all disease cohorts. Not accounting for this loss to follow-up overestimated the magnitude of the association between immigration status and mortality in those with ischaemic stroke (HR of death before vs after accounting for censoring: 0.78 vs 0.83, ratio=0.95; 95% CL 0.93 to 0.97), cancer (0.74 vs 0.78, ratio=0.96; 0.95 to 0.96), and schizophrenia (0.54 vs 0.56, ratio=0.97; 0.96 to 0.98). CONCLUSIONS: Immigrants to Canada have a survival advantage that varies by the disease studied. The magnitude of this advantage is modestly overestimated by not accounting for the higher loss to follow-up in immigrants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.181 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".